Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control

doi:10.3390/books978-3-0365-8061-6

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Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control

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July 2023

260 pages

ISBN978-3-0365-8060-9 (Hardback)
ISBN978-3-0365-8061-6 (PDF)

https://doi.org/10.3390/books978-3-0365-8061-6

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This book is a reprint of the Special Issue Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Public Health & Healthcare

Summary

The Special Issue aimed to bring together scientists working in various branches of control theory to discuss manufacturing control problems that include the following: enterprise control and digital ecosystem creation; the development of identification theory and methodology, and related mathematical problems; parameter, nonparametric, and structure identification and expert analysis; problems regarding selection and data analysis; control systems with an identifier; modeling in intelligent systems; simulation procedures and software; digital identification; reinforcement learning; quantum modeling; intelligent model predictive control; predictive cognitive issues; problems with software quality for complex systems; and global network resources for support processes of modeling and control.

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Keywords

decision-making; psychic and behavioral components of activity; action; result of activity; equilibrium stability; consensus; threshold behavior; cognitive dissonance; conformity; informational control; informational confrontation; soft sensing; multivariate filter; reactive distillation; optimal stochastic control; path planning; 2D random search; interception; external disturbances; invariance; block control principle; decomposition; high-gain factors; sliding mode control; sigmoid function; Gramian method; bilinear system process identification; generalized Lyapunov equation; knowledgebase; associative search models; wavelet analysis; adaptive differential evolution; evolutionary computing; Hammerstein; nonlinear system identification; bilinear systems; eigenmode decomposition; spectral expansions; generalized Lyapunov equation; Gramians; observability; controllability; small-signal analysis; numerical algorithm; tokamak; plasma equilibrium reconstruction; linear plasma models; identification; state observer; LMI; least square technique; deep neural network; parametric uncertainty; robust control; super-stability; regular form; decomposition; dynamic mode decomposition; system identification; Runge–Kutta method; nonparametric model; artificial neural network; Izhikevich artificial neuron; vestibular–ocular reflex; control Lyapunov function; Bayes criterion; Haar wavelets; loss function; mean risk; observable stochastic systems (OStS); stochastic process (StP); wavelet canonical expansion (WLCE); nonparametric identification; dynamic system; integral model; Volterra equations; smoothing cubic splines; selection of the smoothing option; modeling; regularization; inverse problems; balanced identification; error analysis; one-dimensional heat equation; n/a